Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916658019303424 |
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| author | Hu, Tianyi Fu, Qingxu Pu, Zhiqiang Wang, Yuan Qiu, Tenghai |
| author_facet | Hu, Tianyi Fu, Qingxu Pu, Zhiqiang Wang, Yuan Qiu, Tenghai |
| contents | In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them. Unreal-MAP is user-friendly in terms of deployment, modification, and visualization, and all its components are open-source. We also develop an experimental framework compatible with algorithms ranging from rule-based to learning-based provided by third-party frameworks. Lastly, we deploy several SOTA algorithms in example tasks developed via Unreal-MAP, and conduct corresponding experimental analyses. We believe Unreal-MAP can play an important role in the MARL field by closely integrating existing algorithms with user-customized tasks, thus advancing the field of MARL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning Hu, Tianyi Fu, Qingxu Pu, Zhiqiang Wang, Yuan Qiu, Tenghai Artificial Intelligence In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them. Unreal-MAP is user-friendly in terms of deployment, modification, and visualization, and all its components are open-source. We also develop an experimental framework compatible with algorithms ranging from rule-based to learning-based provided by third-party frameworks. Lastly, we deploy several SOTA algorithms in example tasks developed via Unreal-MAP, and conduct corresponding experimental analyses. We believe Unreal-MAP can play an important role in the MARL field by closely integrating existing algorithms with user-customized tasks, thus advancing the field of MARL. |
| title | Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15947 |